Complex and very expensive automated systems are available to locate and detect potential anomalies on semiconductor wafers and liquid crystal displays quickly. However, determining whether these anomalies are actually defects and, if so, obtaining their precise location assessing their impact on the performance of the die when packaged, and diagnosing their cause still requires manual classification. These time-consuming methods are very slow and inconsistent. Results vary with time available, knowledge, training, ability and fatigue of the operator. On review, human experts in average agree with only 62 percent of visual classifications. Experts' agreement with their own classifications diminish markedly over time so that at the end of a month, agreement with their own classifications can be below 50%. These problems, widely observed in diverse task environments, are associated with limitations of human memory and cognition. To ensure consistent quality control for submicron semiconductor manufacturing processes and liquid crystal display manufacturing processes, automatic defect classification, that is directly associated with determination of cause, is necessary.
Types of macro and micro defects include: Gray Spot, Gray Streak, Gray Spot and Gray Streak, Particles, Multi-Layer Structure, Line Break, Subsurface Line, Scratch, Hillocks, Grass, Worm-hole, Starburst, Speedboat, Orange Peel, Resist Gel Defect, Controlled Collapse Chip Connection (C4), Microbridge, Submicron, Micron, Micron Sphere, U. Pattern, Contamination, Protrusion, Break, Intrusion, Nuisance, Mask-Related (Shorts), Haze, Micro-contamination, Crystalline (Stacking Fault), Spots, Break, Reticle, Hard-Defects (Pinholes, Pindots, Extrusions), Semi-Transparent (Resist Residues, Thin Chrome), Registration (Oversized, Undersized, Mislocated), Corner, Extra Metal, Metal Missing and Opens (Pattern Missing).
Because of the dynamic nature of the incidence of semiconductor defects, establishing a set of defect classes or descriptions in advance that covers all possible defects is not possible and efficient. The high degree of customization required for each application adds another dimension to the inherent difficulties. An automatic defect classification system must be able to add and delete defects from a set of reference examples as well as to alter or refine classification rules at any time as production circumstances and requirements change. Furthermore, new defect classes may arise as the process evolves or as new types of equipment are introduced. An automatic defect classification system should also be capable of learning new classes.
Because operators cannot be economically trained to create and edit knowledge bases containing expert system rules, the system should advantageously be able to acquire such rules directly and automatically from images of defects selected as examples. Information about previous and related defects, including images of them, must be made readily available to technical personnel involved in the diagnosis of defects and those that take measures to deal with their causes.
The number of defect examples available for each class of defect can be quite small. Thus, the defect classification and diagnosis system must be able to achieve a high level of accuracy with as few as three examples for each class of defect, and must be capable of being quickly and easily modified.